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from typing import List, Dict, Union, Any
import os
import requests
import re
import json
import fasttext
import torch
from fasttext.FastText import _FastText
from transformers import BertTokenizer
from src.models.base import BaseModel
from src.models.bert_cls import BERTClassifier
from src.enum import (
DucklingDimensionTypes,
DucklingLocaleTypes,
OperatorClassTypes,
)
from src.misc.schemas import PriceExtractionSchema, ProductNamedEntityExtractionSchema
ARABIC_TEXT_PATTERN = r"[\u0600-\u06ff]|[\u0750-\u077f]|[\ufb50-\ufc3f]|[\ufe70-\ufefc]"
class BaseOperatorModel(BaseModel):
def __init__(self, model_ckpt_path: str) -> None:
super().__init__()
self._model = self.load_model(model_ckpt_path)
def __call__(self, input_query: str, *args: Any, **kwds: Any) -> OperatorClassTypes:
return super().__call__(input_query, *args, **kwds)
@abstractmethod
def predict(self, input_query, *args: Any, **kwds: Any) -> OperatorClassTypes:
raise NotImplementedError()
@abstractmethod
def load_model(self, model_ckpt_path):
raise NotImplementedError()
class FasttextOperatorModel(BaseOperatorModel):
def __init__(
self,
model_ckpt_path: str = os.path.join(
"ckpt", "fasttext_operator", "fasttext_operator_cls.bin"
),
opt_thres: float = 0.2,
) -> None:
super().__init__(model_ckpt_path)
self.opt_thres = opt_thres
def load_model(self, model_ckpt_path) -> _FastText:
return fasttext.load_model(model_ckpt_path)
def predict(self, input_query, *args: Any, **kwds: Any) -> OperatorClassTypes:
pred_class, _ = [
res[0] if len(res) else OperatorClassTypes.NONE.value
for res in self._model.predict(
text=input_query, threshold=self.opt_thres, k=1
)
]
for class_type in OperatorClassTypes:
if class_type.value == pred_class:
pred_class = class_type
return pred_class
class BertOperatorModel(BaseOperatorModel):
def __init__(
self,
model_ckpt_path: str = os.path.join("ckpt", "bert_operator"),
) -> None:
model_config: Dict = json.load(
open(os.path.join(model_ckpt_path, "model_config.json"), "r")
)
self._index2class: List[str] = model_config["index2class"]
self._bert_model_name = model_config["bert_model_name"]
self._tokenizer = self.load_tokenizer(bert_model_name=self._bert_model_name)
self._num_classes = len(self._index2class)
self._device = model_config["device"]
self._max_length = model_config["max_length"]
super().__init__(model_ckpt_path)
def load_model(self, model_ckpt_path) -> BERTClassifier:
model = BERTClassifier(self._bert_model_name, self._num_classes).to(
self._device
)
model.eval()
model.load_state_dict(
torch.load(
os.path.join(
model_ckpt_path,
"bert_classifier.pth",
),
map_location=self._device,
)
)
return model
def load_tokenizer(self, bert_model_name: str) -> BertTokenizer:
return BertTokenizer.from_pretrained(bert_model_name)
def predict(self, input_query: str, *args: Any, **kwds: Any) -> OperatorClassTypes:
encoding = self._tokenizer(
input_query,
return_tensors="pt",
max_length=self._max_length,
padding="max_length",
truncation=True,
)
input_ids = encoding["input_ids"].to(self._device)
attention_mask = encoding["attention_mask"].to(self._device)
with torch.no_grad():
outputs = self._model(input_ids=input_ids, attention_mask=attention_mask)
_, pred_class = torch.max(outputs, dim=1)
pred_class = self._index2class[pred_class.item()]
for class_type in OperatorClassTypes:
if class_type.value == pred_class:
pred_class = class_type
return pred_class
class DucklingHTTPOperatorModel(BaseModel):
def __init__(
self,
duckling_host: str = "localhost",
duckling_port: str = "8000",
dim_types: List[DucklingDimensionTypes] = [
DucklingDimensionTypes.AMOUNT_OF_MONEY,
DucklingDimensionTypes.NUMERAL,
DucklingDimensionTypes.ORDINAL,
],
operator_model: BaseOperatorModel = None,
) -> None:
super().__init__()
self._duckling_url = f"http://{duckling_host}:{duckling_port}/parse"
self._dim_types = dim_types
self.operator_model = operator_model
self.currency_pattern_map = {
"SAR": r"\b(ريال سعودي|ر\.س|ريال|SAR|saudi riyal|riyal|sr)\b",
"$": r"\b(دولار امريكي|دولار أمريكي|دولار أمريكى|دولار امريكى|دولار|دولارًا|\$|USD|united states dollar|dollar)\b",
"AED": r"\b(درهم اماراتي|درهم إماراتي|درهم اماراتى|درهم إماراتى|درهم|د\.إ|AED|emirates dirham|emirate dirham|dirham)\b",
"EGP": r"\b(جنيه مصري|جنيه مصرى|جنيه|ج\.م|£|egyptian pound|pound|le|LE|EGP)\b",
}
def predict(
self, input_query, *args: Any, **kwds: Any
) -> ProductNamedEntityExtractionSchema:
input_query = input_query.replace("اغلي", "أغلى").replace("أغلي", "أغلى")
extraction_result = ProductNamedEntityExtractionSchema()
price_extraction_result = PriceExtractionSchema()
extraction_result.sub_category_extraction = input_query
extraction_result.price_extraction = price_extraction_result
operator_class_pred: OperatorClassTypes = self.operator_model(
input_query=input_query
)
locale_type = None
if re.match(pattern=ARABIC_TEXT_PATTERN, string=input_query):
locale_type = DucklingLocaleTypes.AR
else:
locale_type = DucklingLocaleTypes.EN
headers = {"Content-Type": "application/x-www-form-urlencoded"}
if operator_class_pred == OperatorClassTypes.NONE:
return extraction_result
parsed_entities_response = requests.post(
url=self._duckling_url,
headers=headers,
data=self.payload(input_query=input_query, locale=locale_type.value),
).json()
if len(parsed_entities_response):
for parsed_entity in parsed_entities_response:
if "from" in parsed_entity["value"].keys():
price_extraction_result.lower_range = parsed_entity["value"][
"from"
]["value"]
if (
price_extraction_result.unit == ""
or price_extraction_result.unit == "unknown"
):
price_extraction_result.unit = parsed_entity["value"]["from"][
"unit"
]
if "to" in parsed_entity["value"].keys():
price_extraction_result.upper_range = parsed_entity["value"]["to"][
"value"
]
if (
price_extraction_result.unit == ""
or price_extraction_result.unit == "unknown"
):
price_extraction_result.unit = parsed_entity["value"]["to"][
"unit"
]
extraction_result.sub_category_extraction = (
extraction_result.sub_category_extraction.replace(
parsed_entity["body"], ""
)
)
for parsed_entity in parsed_entities_response:
if "value" in parsed_entity["value"].keys():
if (
price_extraction_result.lower_range != -1
and price_extraction_result.upper_range != -1
):
continue
elif (
price_extraction_result.lower_range == -1
and price_extraction_result.upper_range == -1
):
if operator_class_pred == OperatorClassTypes.LE:
price_extraction_result.upper_range = parsed_entity[
"value"
]["value"]
elif operator_class_pred == OperatorClassTypes.EQ:
price_extraction_result.lower_range = parsed_entity[
"value"
]["value"]
price_extraction_result.upper_range = parsed_entity[
"value"
]["value"]
elif operator_class_pred == OperatorClassTypes.GE:
price_extraction_result.lower_range = parsed_entity[
"value"
]["value"]
elif operator_class_pred == OperatorClassTypes.GELE:
numbers_pattern = r"[+-]?\d+\.\d+|\d+"
digits_matches = re.findall(numbers_pattern, input_query)
if len(digits_matches):
digit_matched = None
for digit in digits_matches:
if float(digit) != float(
parsed_entity["value"]["value"]
):
extraction_result.sub_category_extraction = extraction_result.sub_category_extraction.replace(
digit, ""
)
digit_matched = float(digit)
break
if digit_matched:
price_extraction_result.lower_range = min(
parsed_entity["value"]["value"], digit_matched
)
price_extraction_result.upper_range = max(
parsed_entity["value"]["value"], digit_matched
)
else:
price_extraction_result.lower_range = parsed_entity[
"value"
]["value"]
price_extraction_result.upper_range = parsed_entity[
"value"
]["value"]
else:
price_extraction_result.lower_range = parsed_entity[
"value"
]["value"]
price_extraction_result.upper_range = parsed_entity[
"value"
]["value"]
elif price_extraction_result.lower_range != -1:
lower_range = price_extraction_result.lower_range
val = parsed_entity["value"]["value"]
if price_extraction_result.upper_range == -1:
if operator_class_pred == OperatorClassTypes.EQ:
price_extraction_result.upper_range = val
elif operator_class_pred == OperatorClassTypes.LE:
price_extraction_result.upper_range = lower_range
price_extraction_result.lower_range = -1
elif operator_class_pred == OperatorClassTypes.GELE:
price_extraction_result.upper_range = max(
lower_range, val
)
price_extraction_result.lower_range = min(
lower_range, val
)
else:
price_extraction_result.upper_range = max(lower_range, val)
price_extraction_result.lower_range = min(lower_range, val)
elif price_extraction_result.upper_range != -1:
upper_range = price_extraction_result.upper_range
val = parsed_entity["value"]["value"]
if price_extraction_result.lower_range == -1:
if operator_class_pred == OperatorClassTypes.EQ:
price_extraction_result.lower_range = val
elif operator_class_pred == OperatorClassTypes.LE:
price_extraction_result.lower_range = upper_range
price_extraction_result.upper_range = -1
elif operator_class_pred == OperatorClassTypes.GELE:
price_extraction_result.upper_range = max(
upper_range, val
)
price_extraction_result.lower_range = min(
upper_range, val
)
else:
price_extraction_result.upper_range = max(upper_range, val)
price_extraction_result.lower_range = min(upper_range, val)
extraction_result.sub_category_extraction = (
extraction_result.sub_category_extraction.replace(
parsed_entity["body"], ""
)
)
if (
price_extraction_result.unit == ""
or price_extraction_result.unit == "unknown"
):
price_extraction_result.unit = parsed_entity["value"]["unit"]
if price_extraction_result.unit == "unknown":
price_extraction_result.unit = ""
else:
numbers_pattern = r"[+-]?\d+\.\d+|\d+"
digits_matches = [
float(digit) for digit in re.findall(numbers_pattern, input_query)
]
for digit_match in re.findall(numbers_pattern, input_query):
extraction_result.sub_category_extraction = (
extraction_result.sub_category_extraction.replace(digit_match, "")
)
if len(digits_matches):
if operator_class_pred == OperatorClassTypes.LE:
price_extraction_result.upper_range = max(digits_matches)
elif operator_class_pred == OperatorClassTypes.EQ:
price_extraction_result.upper_range = digits_matches[0]
price_extraction_result.lower_range = digits_matches[0]
elif operator_class_pred == OperatorClassTypes.GE:
price_extraction_result.lower_range = min(digits_matches)
elif operator_class_pred == OperatorClassTypes.GELE:
price_extraction_result.upper_range = max(digits_matches)
price_extraction_result.lower_range = min(digits_matches)
for currency, cur_pattern in self.currency_pattern_map.items():
currency_matches = re.findall(cur_pattern, input_query, re.IGNORECASE)
if len(currency_matches):
price_extraction_result.unit = currency
extraction_result.sub_category_extraction = (
extraction_result.sub_category_extraction.replace(
currency_matches[0], ""
)
)
break
extraction_result.sub_category_extraction = " ".join(
extraction_result.sub_category_extraction.split()
)
for currency, cur_pattern in self.currency_pattern_map.items():
currency_matches = re.findall(
cur_pattern, price_extraction_result.unit, re.IGNORECASE
)
if len(currency_matches):
price_extraction_result.unit = currency
break
return extraction_result
def payload(self, input_query: str, locale: str) -> Dict[str, Union[List, str]]:
return {
"text": input_query,
"locale": locale,
"dims": json.dumps([dim_type.value for dim_type in self._dim_types]),
}
if __name__ == "__main__":
model = DucklingHTTPOperatorModel()
# print(model(input_query="360"))
# print(model(input_query="يساوي 12 ريال").model_dump())
# # print(model(input_query="من 12 ريال"))
# # print(model(input_query="أقل 12 ريال"))
# # print(model(input_query="أكثر من 12 ريال"))
# print(model(input_query="أكثر من 12 إلى 20 ريال"))
# # print(model(input_query="عطر ديور ارخص من ٢٠٠").model_dump())
# print(model(input_query="اقل من 200 ريال").model_dump())
# print(model(input_query="أرخص من 200").model_dump())
# print(model(input_query="cheaper than 200 ").model_dump())
# pattern = r"\b(ريال|دولار|درهم|دينار|يورو|جنيه|ليرة|د\.إ|ريال سعودي|ر\.س|جنيه مصري|جنيه مصرى|دولار امريكي|دولار امريكى|درهم اماراتي|درهم إماراتي|درهم اماراتى|درهم إماراتى|ج\.م|\$|€|£|USD|AED|SAR|EUR|GBP|usd|aed|sar|eur|saudi riyal|riyal|sr|egyptian pound|pound|le|LE|emirates dirham|dirham|emirate dirham|dollar|united states dollar)\b"
pattern = r"\b(ريال سعودي|ر\.س|جنيه مصري|جنيه مصرى|دولار امريكي|دولار أمريكي|دولار أمريكى|دولار امريكى|درهم اماراتي|درهم إماراتي|درهم اماراتى|درهم إماراتى|ريال|دولار|درهم|دينار|يورو|جنيه|ليرة|د\.إ|ج\.م|\$|€|£|USD|AED|SAR|EUR|GBP|usd|aed|sar|eur|saudi riyal|egyptian pound|riyal|sr|pound|le|LE|emirates dirham|emirate dirham|dirham|united states dollar|dollar)\b"
# Example text containing different currencies in Arabic and English
text = """
سعر المنتج هو 120 ريال سعودي.
التكلفة الكلية 50 دولار أمريكي.
The laptop costs $500 USD.
سعر الهاتف هو 250 درهم إماراتي.
سعر الساعة 99.99€.
"""
# Find all matches of currencies in the text
matches = re.findall(pattern, text, re.IGNORECASE)
print(matches)
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